In [1]:
# Importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
# Loading excel file into dataframe(df)
df = pd.read_csv(r'C:\Users\Lenovo\OneDrive\Y2S3\Introduction to Data Science\Assignment\Dataset\owid-covid-data.csv')
In [3]:
# Displaying the top records of the dataset
df.head()
Out[3]:
iso_code continent location date total_cases new_cases new_cases_smoothed total_deaths new_deaths new_deaths_smoothed ... extreme_poverty cardiovasc_death_rate diabetes_prevalence female_smokers male_smokers handwashing_facilities hospital_beds_per_thousand life_expectancy human_development_index excess_mortality
0 AFG Asia Afghanistan 2020-02-24 1.0 1.0 NaN NaN NaN NaN ... NaN 597.029 9.59 NaN NaN 37.746 0.5 64.83 0.511 NaN
1 AFG Asia Afghanistan 2020-02-25 1.0 0.0 NaN NaN NaN NaN ... NaN 597.029 9.59 NaN NaN 37.746 0.5 64.83 0.511 NaN
2 AFG Asia Afghanistan 2020-02-26 1.0 0.0 NaN NaN NaN NaN ... NaN 597.029 9.59 NaN NaN 37.746 0.5 64.83 0.511 NaN
3 AFG Asia Afghanistan 2020-02-27 1.0 0.0 NaN NaN NaN NaN ... NaN 597.029 9.59 NaN NaN 37.746 0.5 64.83 0.511 NaN
4 AFG Asia Afghanistan 2020-02-28 1.0 0.0 NaN NaN NaN NaN ... NaN 597.029 9.59 NaN NaN 37.746 0.5 64.83 0.511 NaN

5 rows × 60 columns

In [4]:
df_us = df[df['iso_code'] == 'USA']
In [5]:
df_us.head()
Out[5]:
iso_code continent location date total_cases new_cases new_cases_smoothed total_deaths new_deaths new_deaths_smoothed ... extreme_poverty cardiovasc_death_rate diabetes_prevalence female_smokers male_smokers handwashing_facilities hospital_beds_per_thousand life_expectancy human_development_index excess_mortality
91593 USA North America United States 2020-01-22 1.0 NaN NaN NaN NaN NaN ... 1.2 151.089 10.79 19.1 24.6 NaN 2.77 78.86 0.926 NaN
91594 USA North America United States 2020-01-23 1.0 0.0 NaN NaN NaN NaN ... 1.2 151.089 10.79 19.1 24.6 NaN 2.77 78.86 0.926 NaN
91595 USA North America United States 2020-01-24 2.0 1.0 NaN NaN NaN NaN ... 1.2 151.089 10.79 19.1 24.6 NaN 2.77 78.86 0.926 NaN
91596 USA North America United States 2020-01-25 2.0 0.0 NaN NaN NaN NaN ... 1.2 151.089 10.79 19.1 24.6 NaN 2.77 78.86 0.926 NaN
91597 USA North America United States 2020-01-26 5.0 3.0 NaN NaN NaN NaN ... 1.2 151.089 10.79 19.1 24.6 NaN 2.77 78.86 0.926 0.75

5 rows × 60 columns

In [6]:
df_us['CFR'] = (df_us['total_cases'] / df_us['total_deaths'])*100
df_us['CFR_per_million'] = (df_us['total_cases_per_million'] / df_us['total_deaths_per_million'])*100
df_us['impact'] = (df_us['total_cases'] * 0.1 + df_us['hosp_patients'] * 0.2 + df_us['icu_patients'] * 0.3 + df_us['total_deaths'] * 0.4)
df_us['impact_per_million'] = (df_us['total_cases_per_million'] * 0.1 + df_us['hosp_patients_per_million'] * 0.2 + df_us['icu_patients_per_million'] * 0.3 + df_us['total_deaths_per_million'] * 0.4)

<ipython-input-6-77b0c849ba27>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_us['CFR'] = (df_us['total_cases'] / df_us['total_deaths'])*100
<ipython-input-6-77b0c849ba27>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_us['CFR_per_million'] = (df_us['total_cases_per_million'] / df_us['total_deaths_per_million'])*100
<ipython-input-6-77b0c849ba27>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_us['impact'] = (df_us['total_cases'] * 0.1 + df_us['hosp_patients'] * 0.2 + df_us['icu_patients'] * 0.3 + df_us['total_deaths'] * 0.4)
<ipython-input-6-77b0c849ba27>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_us['impact_per_million'] = (df_us['total_cases_per_million'] * 0.1 + df_us['hosp_patients_per_million'] * 0.2 + df_us['icu_patients_per_million'] * 0.3 + df_us['total_deaths_per_million'] * 0.4)
In [7]:
# Update graph font size
plt.rcParams.update({'font.size': 22})
In [8]:
# Plotting heatmap for EDA
plt.figure(figsize = (20,25))
corr = df_us.corr()
sns.heatmap(corr[['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred', 'new_vaccinations_smoothed_per_million', 'CFR', 'CFR_per_million']], cmap = 'Blues', annot = True)
Out[8]:
<AxesSubplot:>
In [9]:
# Plotting scatter plots: vaccination variables against total_cases
plt.figure()
dep = 'total_cases'
ind = ['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred','new_vaccinations_smoothed_per_million']
for i, j in enumerate(ind):
    correlation = df_us[j].corr(df_us[dep])
    ax = df_us.plot.scatter(x=j, y=dep, c='DarkBlue', title="x: {x_name}; y: {y_name}; corr: {corr}".format(x_name = j, y_name = dep, corr = correlation), figsize=(30,45), fontsize=22)

<Figure size 432x288 with 0 Axes>
In [10]:
# Plotting scatter plots: vaccination variables against total_cases_per_million
plt.figure()
dep = 'total_cases_per_million'
ind = ['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred','new_vaccinations_smoothed_per_million']
for i, j in enumerate(ind):
    correlation = df_us[j].corr(df_us[dep])
    ax = df_us.plot.scatter(x=j, y=dep, c='DarkBlue', title="x: {x_name}; y: {y_name}; corr: {corr}".format(x_name = j, y_name = dep, corr = correlation), figsize=(30,45), fontsize=22)

<Figure size 432x288 with 0 Axes>
In [11]:
# Plotting scatter plots: impact against vaccination variables
# impact = total_cases * 0.1 + hosp_patients * 0.2 + icu_patients * 0.3 + total_deaths * 0.4
plt.figure()
dep = 'impact'
ind = ['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred','new_vaccinations_smoothed_per_million']
for i, j in enumerate(ind):
    correlation = df_us[j].corr(df_us[dep])
    ax = df_us.plot.scatter(x=j, y=dep, c='DarkBlue', title="x: {x_name}; y: {y_name}; corr: {corr}".format(x_name = j, y_name = dep, corr = correlation), figsize=(30,45), fontsize=22)

<Figure size 432x288 with 0 Axes>
In [12]:
# Plotting scatter impact_per_million against vaccination variables
# impact_per_million = total_cases_per_million * 0.1 + hosp_patients_per_million * 0.2 + icu_patients_per_million * 0.3 + total_deaths_per_million * 0.4
plt.figure()
dep = 'impact_per_million'
ind = ['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred','new_vaccinations_smoothed_per_million']
for i, j in enumerate(ind):
    correlation = df_us[j].corr(df_us[dep])
    ax = df_us.plot.scatter(x=j, y=dep, c='DarkBlue', title="x: {x_name}; y: {y_name}; corr: {corr}".format(x_name = j, y_name = dep, corr = correlation), figsize=(30,45), fontsize=22)

<Figure size 432x288 with 0 Axes>
In [ ]: